Simo Särkkä
12 papers · 2012–2025 · 5 venues · across top CS/AI venues
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🗺️ Taxonomy Completionist (20) 🏃 Academic Marathon · Half 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge · Silver 🌍 Conference Polyglot · Silver
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Keyword Collector
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Research topics
Machine Learning
(10)
Optimization & Theory
(9)
Artificial Intelligence
(6)
Mathematics & Optimization
(6)
Bayesian & Probabilistic · Artificial Intelligence
(6)
Bayesian & Probabilistic · Machine Learning
(4)
Core Methods
(3)
Mathematics
(3)
Probability
(2)
Deep Learning
(1)
Reinforcement Learning
(1)
Learning Paradigms
(1)
Learning Types
(1)
Methods
(1)
Models
(1)
Keywords
gaussian process
(5)
bayesian inference
(5)
state space model
(5)
stochastic differential equation
(3)
kalman filter
(2)
sequential monte carlo
(2)
gaussian process regression
(2)
kalman filtering
(2)
particle filter
(2)
parallel computing
(2)
markov chain monte carlo
(1)
diffusion process
(1)
time series
(1)
spatio-temporal modeling
(1)
natural gradient
(1)
uncertainty quantification
(1)
state estimation
(1)
expectation propagation
(1)
diffusion processes
(1)
system identification
(1)
Papers
Conditioning diffusion models by explicit forward-backward bridging
AISTATS 2025
Nesting Particle Filters for Experimental Design in Dynamical Systems
ICML 2024
Parallel-in-Time Probabilistic Numerical ODE Solvers
JMLR 2024
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
JMLR 2023
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother
JMLR 2022
A Bayes-Sard Cubature Method
NeurIPS 2018
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
AISTATS 2016
State Space Methods for Efficient Inference in Student-t Process Regression
AISTATS 2015
Batch Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression
RSS 2014
Explicit Link Between Periodic Covariance Functions and State Space Models
AISTATS 2014
Infinite-Dimensional Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression
AISTATS 2012
The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes
NeurIPS 2012